.png){kind=icon}
返回
A Two-Phase Multiobjective Sparse Unmixing Approach for Hyperspectral Data
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2017.2751060.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the sparse unmixing becoming increasingly popular recently, some advanced regularization algorithms have been proposed for settling this problem. However, they are limited by their decision ahead of solution attribute, i.e., the regularization parameters must be preset before the solution is obtained. In this paper, the sparse unmixing problem is first formulated as a two-phase multiobjective problem. The first phase simultaneously minimizes the unmixing residuals and the number of estimated endmembers for automatically finding the real active endmembers from the spectral library. A decomposition-based endmember selection algorithm considering the gene exchange in the population is specially designed for better and quicker search of the decision space. This algorithm can obtain a set of nondominated solutions for better decision of the active endmembers, which are important for the subsequent calculation of the abundance matrix. The second phase concurrently minimizes the unmixing residuals and the total variation term for estimating a preferable abundance matrix. A local search strategy based on the multiplicative update rule is designed in the evolution process for better approximation of the Pareto front. The experimental results on the synthetic as well as the real data reveal that the proposed framework has a better performance in finding the real active endmembers and estimating their corresponding abundances than some advanced regularization algorithms.
Keyword:
Better decision
decision ahead of solution
hyperspectral unmixing
multiplicative update (MU) rule
two-phase multiobjective sparse unmixing (Tp-MoSU)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
MUSIC-CSR: Hyperspectral Unmixing via Multiple Signal Classification and Collaborative Sparse RegressionMusic-csr: 通过多信号分类和协同稀疏回归实现高光谱分解
OAAI
.png){kind=icon}
OAAI分享收藏Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery基于稀疏分量分析的高光谱遥感图像光谱盲分解
PREAI
分享收藏
分享收藏Spatial Group Sparsity Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing用于高光谱分解的空间群稀疏正则化非负矩阵分解
PREAI
分享收藏
查看更多内容